To capture the spatial correlations among different entities at a specific timestamp, we employ attention mechanism [1]}.
The idea is to determine, in order to predict the features of an entity, i.e., a vertex, how much should we consider the features of the vertex's neighbour vertices. We may consider different neighbour vertices differently at different timestamps.
Specifically, for each vertex \(i\) , we compute an attention score w.r.t. to each vertex in \(NB(i)=\lbrace j|E[i,j]=1\rbrace \cup \lbrace i\rbrace \) , i.e., vertex \(i\) 's neighbour vertices and itself.
